摘要
目的:探讨特发性卵巢早衰(POF)与人们的生活方式、行为习惯、工作生活环境等因素的关系,为POF的预防及治疗提供科学依据。方法:选择于我院诊治的特发性POF患者105例(特发性POF组)和月经规则、性激素检查在正常范围内的年龄匹配的110例健康妇女(对照组)为调查对象,根据填写的《卵巢早衰发病相关因素调查问卷》对两组的临床特征进行比较,并采用单因素、多因素Logistic回归法分析特发性POF的相关危险因素。结果:对单因素分析有统计学意义的9个变量进行多因素Logistic回归分析,结果显示:引起特发性POF的危险因素有使用染发剂(OR=5.641,95%CI2.441~13.035)、经常因小事烦恼(OR=4.811,95%CI1.950~11.873)、经常感到疲倦(OR=8.827,95%CI2.758~28.249)、有害物质接触史(OR=9.095,95%CI1.012~81.759);特发性POF的保护因素是经常锻炼(OR=0.439,95%CI0.200~0.964)。结论:养成良好的生活习惯、尽量避免接触有害物质、调节情绪、保持心态平衡和经常锻炼可能有利于降低特发性POF的发病率。
Objective:To investigate the association of life style,habit,living conditions and working conditions with the onset of idiopathic premature ovarian failure(POF),in order to find all the possible risk and protective factors of idiopathic POF,and to provide scientific basis for further prevention and treatment of idiopathic POF,Methods:105 idiopathic POF patients treated in our hospital and 110 women with normal menstruation of infertility women as control group were included in the study.Using self-designed questionnaire,necessary data were collected by the professional staff from Shenzhen Maternal&Child Health Institute.One-way and multiple logistic regression were used to estimate idiopathic POF risk.Results:By the multiple logistic regression analysis,it was revealed that there were five significant risk factors for idiopathic POF.Hair staining(OR=5.641,95%CI 2.441-13.035),unhealthy emotions(OR=4.811,95%CI 1.950-11.873),always felt tired(OR=8.827,95%CI 2.758-28.249),contacting poisons(OR=9.095,95%CI 1.012-81.759)were the risk factors.While,taking more exercises(OR=0.439,95%CI 0.200-0.964)were the protective factors of POF.Conclusions.Good life style,avoiding exposure to toxic and hazardous substances and keeping healthy mind would be effective to decrease the opportunity of idiopathic POF.
出处
《实用妇产科杂志》
CAS
CSCD
北大核心
2013年第2期133-136,共4页
Journal of Practical Obstetrics and Gynecology
基金
深圳市科技计划项目(编号:201102094,201202073)
广东省社会发展领域科技计划项目(2011年)
关键词
卵巢早衰
特发性
危险因素
保护因素
多因素回归分析
Premature ovarian failure
Ldiopathic
Risk factors
Protective factors
Multiple logistic regres-sion
